{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "849c7781",
   "metadata": {},
   "source": [
    "# 基于AReaL强化学习训练长程搜索智能体（Search Agent）\n",
    "\n",
    "本教程利用AReaL中的基本组件快速搭建一个强化学习流程，用来训练一个可以进行长程搜索的智能体。\n",
    "\n",
    "该教程包括以下步骤：\n",
    "1. 实验准备（包括从yaml加载实验配置，配置环境变量，启动SGLang服务器，启动本地RAG服务器，加载训练数据集）\n",
    "2. 定义简单的工作流，多次调用搜索工具；\n",
    "3. 每次生成**多条**轨迹（i.e., GRPO)；\n",
    "4. 测试工作流；\n",
    "5. 将工作流接入端到端GRPO强化学习训练；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badf1529",
   "metadata": {},
   "source": [
    "## 实验准备\n",
    "### 加载实验配置\n",
    "\n",
    "通过`load_expr_config`加载预定义的asearcher基于local RAG训练的yaml实验配置模板。\n",
    "\n",
    "实验配置模板内配置了优化器、模板、学习率等参数，可以直接使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e88f19db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import asdict, dataclass, field\n",
    "\n",
    "from areal.api.cli_args import GRPOConfig, load_expr_config\n",
    "\n",
    "@dataclass\n",
    "class AgentRLConfig(GRPOConfig):\n",
    "    max_turns: int = field(\n",
    "        default=128,\n",
    "        metadata={\"help\": \"maximum number of turns per trajectory\"}\n",
    "    )\n",
    "\n",
    "args = [\"--config\", \"examples/configs/search-agent/local_1.5b_example.yaml\"]\n",
    "config, _ = load_expr_config(args, AgentRLConfig)\n",
    "config: AgentRLConfig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c1c498",
   "metadata": {},
   "source": [
    "### 配置环境变量\n",
    "\n",
    "我们预先分配SGLang服务器和PyTorch分布式启动的IP地址和端口，并设置相应的环境变量。\n",
    "\n",
    "这些环境变量会在引擎初始化时被读取。\n",
    "\n",
    "***在非notebook环境下，这些环境变量会被launcher设置，用户无需自行设置。***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "552e4d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from areal.utils.network import find_free_ports\n",
    "\n",
    "SGLANG_PORT, MASTER_PORT = 11451, 14514\n",
    "\n",
    "SGLANG_HOST = \"127.0.0.1\"\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# Environment variables used by inference/train engines\n",
    "import os\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "os.environ[\"AREAL_LLM_SERVER_ADDRS\"] = f\"{SGLANG_HOST}:{SGLANG_PORT}\"\n",
    "os.environ[\"MASTER_ADDR\"] = \"127.0.0.1\"\n",
    "os.environ[\"MASTER_PORT\"] = str(MASTER_PORT)\n",
    "os.environ[\"RANK\"] = str(0)\n",
    "os.environ[\"WORLD_SIZE\"] = str(1)\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
    "os.environ[\"LOCAL_RANK\"] = str(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6e9329",
   "metadata": {},
   "source": [
    "### 启动SGLang服务器\n",
    "\n",
    "AReaL默认采用训推分离式架构，推理和训练异步执行，能够打满GPU资源、快速完成端到端训练。\n",
    "\n",
    "在这个样例中，强化学习的算法编排（GRPO）运行在GPU 0上。\n",
    "\n",
    "GPU 1运行一个推理服务器，强化学习的算法编排可以向GPU 1上的推理服务发送生成请求。\n",
    "\n",
    "下面的代码块在GPU 1上启动对应的推理服务。\n",
    "\n",
    "本次教程中使用`Qwen/Qwen2.5-1.5B`作为例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c015761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sglang process is launched\n"
     ]
    }
   ],
   "source": [
    "# 启动sglang server\n",
    "from areal.api.cli_args import SGLangConfig\n",
    "from areal.utils.network import find_free_ports\n",
    "\n",
    "config.sglang.log_level = \"info\"\n",
    "config.sglang.decode_log_interval = 10\n",
    "sglang_cmd = SGLangConfig.build_cmd(\n",
    "    config.sglang,\n",
    "    tp_size=1,\n",
    "    base_gpu_id=1,\n",
    "    host=SGLANG_HOST,\n",
    "    port=SGLANG_PORT,\n",
    ")\n",
    "sglang_process = subprocess.Popen(\n",
    "    sglang_cmd,\n",
    "    shell=True,\n",
    "    stdout=sys.stdout,\n",
    "    stderr=sys.stderr,\n",
    ")\n",
    "\n",
    "print(\"sglang process is launched\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529b6c5a",
   "metadata": {},
   "source": [
    "### 加载训练数据集\n",
    "\n",
    "使用HuggingFace `datasets` 包加载训练数据集，并查看数据集格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97726ee9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset is at /root/autodl-tmp/hub/datasets--inclusionAI--ASearcher-train-data/snapshots/2e1b758bd53a36c28cbe1d49776af35f95a9a4b0/ASearcher-Base-35k.jsonl\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3400db814504987bef37f59fa3ad92b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> dataset column names: ['question', 'answer', 'source', 'aug_answer', 'qid']\n",
      ">>> example data: {'question': 'What nationality is the director of film Queensland (Film)?', 'answer': ['Australian'], 'source': 'opensource', 'aug_answer': ['Aussie', 'Australian national', 'From Australia', 'Australian citizen', 'Australians', 'Australian'], 'qid': 1}\n"
     ]
    }
   ],
   "source": [
    "# load search dataset\n",
    "from datasets import load_dataset\n",
    "\n",
    "print(\"dataset is at {}\".format(config.train_dataset.path))\n",
    "dataset = load_dataset(\n",
    "        path=\"json\",\n",
    "        split=\"train\",\n",
    "        data_files=config.train_dataset.path,\n",
    "    )\n",
    "print(f\">>> dataset column names: {dataset.column_names}\")\n",
    "print(f\">>> example data: {dataset[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4050248e",
   "metadata": {},
   "source": [
    "### 导入必要的包和模块\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d915abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import os\n",
    "import sys\n",
    "import uuid\n",
    "import json\n",
    "import time\n",
    "import torch\n",
    "import torch.distributed as dist\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from datasets.distributed import split_dataset_by_node\n",
    "from tensordict import TensorDict\n",
    "from transformers import PreTrainedTokenizerFast, AutoTokenizer\n",
    "\n",
    "from areal.api.cli_args import (\n",
    "    GenerationHyperparameters,\n",
    "    load_expr_config,\n",
    ")\n",
    "from areal.api.io_struct import (\n",
    "    FinetuneSpec,\n",
    "    LLMRequest,\n",
    "    WeightUpdateMeta,\n",
    ")\n",
    "from areal.api.engine_api import InferenceEngine\n",
    "from areal.engine.ppo.actor import FSDPPPOActor\n",
    "from areal.engine.sglang_remote import RemoteSGLangEngine\n",
    "from areal.utils.data import concat_padded_tensors\n",
    "from areal.api.io_struct import (\n",
    "    AllocationMode,\n",
    "    FinetuneSpec,\n",
    "    LLMRequest,\n",
    "    WeightUpdateMeta,\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path)\n",
    "if tokenizer.pad_token_id is None:\n",
    "    tokenizer.pad_token_id = tokenizer.eos_token_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5c2ec9",
   "metadata": {},
   "source": [
    "使用`torchdata.stateful_dataloader.StatefulDataLoader` 作为dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f908923c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> The type of a batch is: <class 'list'>\n",
      "\n",
      ">>> Each piece of data has keys: dict_keys(['question', 'answer', 'source', 'aug_answer', 'qid'])\n",
      "\n",
      ">>> Example input question: What daily political journal was Matthew Cooper the White House editor for?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# setup dataloader\n",
    "\n",
    "from torchdata.stateful_dataloader import StatefulDataLoader\n",
    "dataloader = StatefulDataLoader(\n",
    "    dataset,\n",
    "    batch_size=config.train_dataset.batch_size,\n",
    "    shuffle=True,\n",
    "    collate_fn=lambda x: x,\n",
    "    drop_last=True,\n",
    ")\n",
    "\n",
    "from itertools import cycle\n",
    "\n",
    "data_generator = cycle(dataloader)\n",
    "\n",
    "ft_spec = FinetuneSpec(\n",
    "    total_train_epochs=config.total_train_epochs,\n",
    "    dataset_size=len(dataloader) * config.train_dataset.batch_size,\n",
    "    train_batch_size=config.train_dataset.batch_size,\n",
    ")\n",
    "\n",
    "batch = next(data_generator)\n",
    "print(f\">>> The type of a batch is: {type(batch)}\\n\")\n",
    "print(f\">>> Each piece of data has keys: {batch[0].keys()}\\n\")\n",
    "print(f\">>> Example input question: {batch[0]['question']}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ccf337",
   "metadata": {},
   "source": [
    "### 配置搜索工具"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49337e1",
   "metadata": {},
   "source": [
    "本地RAG服务器部署方式请见：[ASearcher 仓库](https://github.com/inclusionAI/ASearcher/blob/main/docs/training.md#b-training-a-search-agent-with-local-knowledge-base) - Step 2.\n",
    "\n",
    "通过5001端口给本地RAG服务器发送查询，并接收结果。\n",
    "\n",
    "如下展示了一个搜索关键词\"China\"的例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295682fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup tool\n",
    "\n",
    "import asyncio\n",
    "import aiohttp\n",
    "import json\n",
    "\n",
    "TOOL_SERVER_ADDR = \"localhost:5001\"\n",
    "\n",
    "async def call_search_tool(**req_meta):\n",
    "    async with aiohttp.ClientSession() as session:\n",
    "        async with session.post(\n",
    "            f\"http://{TOOL_SERVER_ADDR}/retrieve\", json=req_meta, timeout=aiohttp.ClientTimeout(total=120, sock_connect=120)\n",
    "        ) as response:\n",
    "            response.raise_for_status()\n",
    "            res = await response.json()\n",
    "            return res[\"result\"]\n",
    "\n",
    "result = (await call_search_tool(queries=[\"China\"], topk=5, return_scores=False))[0]\n",
    "print(json.dumps(result, indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3437dc48",
   "metadata": {},
   "source": [
    "## 定义简单的智能体工作流"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4509e780",
   "metadata": {},
   "source": [
    "### 模型输出\n",
    "\n",
    "使用prompt控制模型输出，模型输出应遵循特定格式：\n",
    "- `<think></think>` 包含模型思考过程\n",
    "- `<search></search>` 包含给本地RAG服务器的查询\n",
    "- `<answer></answer>` 包含模型输出的答案\n",
    "\n",
    "此外，使用`<information></information>` 包含RAG服务器返回的查询内容。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4c004f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> PROMPT: A conversation between User and Assistant. The user asks a question, and the Assistant answers it. The Assistant analyzes the given question and information in the mind, retains important relevant information, calls a search engine to find necessary information, accesses web pages with certain urls, and provides the user with the answer. The Assistant conducts search by <search> query </search> and the top search results will be returned between <information> and </information>. The reasoning processes are enclosed within <think> </think>. Finally, the Assistant provides answer inside <answer> and </answer>, i.e. <answer> answer here </answer>. If there are multiple queries, ensure all answers are enclosed within <answer> </answer>, seperated with comma. \n",
      "\n",
      "User: \n",
      "In January 1812, during the blockade of Ciudad Rodrigo, which well-trained British regiment, noted for its proficiency in mobile infantry techniques, played a crucial role in the storming of the gap near the San Francisco Convent and sustained significant losses, including 39 officers and 700 men?\n",
      "\n",
      "Assistant:\n",
      "<think>\n"
     ]
    }
   ],
   "source": [
    "PROMPT_TEMPLATE=\"\"\"A conversation between User and Assistant. The user asks a question, and the Assistant answers it. The Assistant analyzes the given question and information in the mind, retains important relevant information, calls a search engine to find necessary information, accesses web pages with certain urls, and provides the user with the answer. The Assistant conducts search by <search> query </search> and the top search results will be returned between <information> and </information>. The reasoning processes are enclosed within <think> </think>. Finally, the Assistant provides answer inside <answer> and </answer>, i.e. <answer> answer here </answer>. If there are multiple queries, ensure all answers are enclosed within <answer> </answer>, seperated with comma. \n",
    "\n",
    "User: \n",
    "{question}\n",
    "\n",
    "Assistant:\n",
    "<think>\"\"\"\n",
    "\n",
    "batch = next(data_generator)\n",
    "prompt = PROMPT_TEMPLATE.format(question=batch[0][\"question\"])\n",
    "\n",
    "print(f\">>> PROMPT: {prompt}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdcaeca7",
   "metadata": {},
   "source": [
    "通过`RemoteSGlangEngine`向已经启动的SGLang服务器发送生成请求，测试上述prompt控制下的模型输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf9a76c6-bd77-41c7-a12b-8d8b7fc91229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_UnixSelectorEventLoop running=True closed=False debug=False>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asyncio.get_running_loop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3dbb97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[37m20250818-11:31:49.476 areal.engine.sglang_remote INFO: Waiting for server ready...\u001b[0m\n",
      "\u001b[37m20250818-11:31:49.479 areal.engine.sglang_remote INFO: Servers are all ready!\u001b[0m\n",
      ">>> prompt str: A conversation between User and Assistant. The user asks a question, and the Assistant answers it. The Assistant analyzes the given question and information in the mind, retains important relevant information, calls a search engine to find necessary information, accesses web pages with certain urls, and provides the user with the answer. The Assistant conducts search by <search> query </search> and the top search results will be returned between <information> and </information>. The reasoning processes are enclosed within <think> </think>. Finally, the Assistant provides answer inside <answer> and </answer>, i.e. <answer> answer here </answer>. If there are multiple queries, ensure all answers are enclosed within <answer> </answer>, seperated with comma. \n",
      "\n",
      "User: \n",
      "In January 1812, during the blockade of Ciudad Rodrigo, which well-trained British regiment, noted for its proficiency in mobile infantry techniques, played a crucial role in the storming of the gap near the San Francisco Convent and sustained significant losses, including 39 officers and 700 men?\n",
      "\n",
      "Assistant:\n",
      "<think>\n",
      ">>> generated:  Rajasthan </think> \n",
      "\n",
      "<search> krishna old memoirs </search>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# initialize inference engine\n",
    "rollout_engine = RemoteSGLangEngine(config.rollout)\n",
    "rollout_engine.initialize(None, None)\n",
    "\n",
    "# generation config\n",
    "gconfig = GenerationHyperparameters(max_new_tokens=512, stop=[\"</search>\", \"</answer>\", \"</access>\"])\n",
    "\n",
    "# tokenize the prompt\n",
    "input_ids = tokenizer([prompt], add_special_tokens=False)[\"input_ids\"][0]\n",
    "req = LLMRequest(rid=uuid.uuid4().hex, input_ids=input_ids, gconfig=gconfig)\n",
    "\n",
    "# generate rollout with inference engine\n",
    "resp = await rollout_engine.agenerate(req)\n",
    "completion_str = tokenizer.decode(resp.output_tokens)\n",
    "\n",
    "# logging\n",
    "print(f\">>> prompt str: {tokenizer.decode(resp.input_tokens)}\")\n",
    "print(f\">>> generated: {tokenizer.decode(resp.output_tokens)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acdfb21b",
   "metadata": {},
   "source": [
    "#### 解析智能体工具调用\n",
    "\n",
    "定义`parse_search_query`函数从模型输出解析调用搜索工具的查询。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a064fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> input:  <think> I would like to search for AI.</think>\n",
      "<search> Artificial Intelligence </search>\n",
      ">>> search query:  Artificial Intelligence\n"
     ]
    }
   ],
   "source": [
    "# parse tool calling\n",
    "\n",
    "import re\n",
    "\n",
    "def parse_search_query(text):\n",
    "    pattern = r\"<search>(.*?)</search>\"\n",
    "    matches = re.findall(pattern, text, re.DOTALL)\n",
    "    if matches:\n",
    "        return matches[-1].strip()\n",
    "    return None\n",
    "\n",
    "test_tool_str = \"<think> I would like to search for AI.</think>\\n<search> Artificial Intelligence </search>\"\n",
    "print(\">>> input: \", test_tool_str)\n",
    "print(\">>> search query: \", parse_search_query(test_tool_str))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55170a33",
   "metadata": {},
   "source": [
    "在模型输出上测试工具调用解析函数`parse_search_query`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fed6fe9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> prompt str: A conversation between User and Assistant. The user asks a question, and the Assistant answers it. The Assistant analyzes the given question and information in the mind, retains important relevant information, calls a search engine to find necessary information, accesses web pages with certain urls, and provides the user with the answer. The Assistant conducts search by <search> query </search> and the top search results will be returned between <information> and </information>. The reasoning processes are enclosed within <think> </think>. Finally, the Assistant provides answer inside <answer> and </answer>, i.e. <answer> answer here </answer>. If there are multiple queries, ensure all answers are enclosed within <answer> </answer>, seperated with comma. \n",
      "\n",
      "User: \n",
      "In January 1812, during the blockade of Ciudad Rodrigo, which well-trained British regiment, noted for its proficiency in mobile infantry techniques, played a crucial role in the storming of the gap near the San Francisco Convent and sustained significant losses, including 39 officers and 700 men?\n",
      "\n",
      "Assistant:\n",
      "<think>\n",
      ">>> generated:  Let's analyze the situation. It's important to know which type of regiment was mentioned in the question. </think>\n",
      "<search> regiment well-trained British </search>\n",
      "\n",
      ">>> search query: regiment well-trained British\n"
     ]
    }
   ],
   "source": [
    "# generate rollout with inference engine\n",
    "resp = await rollout_engine.agenerate(req)\n",
    "completion_str = tokenizer.decode(resp.output_tokens)\n",
    "\n",
    "# logging\n",
    "print(f\">>> prompt str: {tokenizer.decode(resp.input_tokens)}\")\n",
    "print(f\">>> generated: {tokenizer.decode(resp.output_tokens)}\")\n",
    "print(f\">>> search query: {parse_search_query(completion_str)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb4f635",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "#### 解析智能体答案\n",
    "\n",
    "定义 `parse_answer` 函数从模型输出中解析模型答案。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80396e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> input:  <think> I already found the answer! </think>\n",
      "<answer> 1997 </answer>\n",
      ">>> answer:  1997\n"
     ]
    }
   ],
   "source": [
    "# parse answer\n",
    "\n",
    "def parse_answer(text):\n",
    "    pattern = r\"<answer>(.*?)</answer>\"\n",
    "    matches = re.findall(pattern, text, re.DOTALL)\n",
    "    if matches:\n",
    "        return matches[-1].strip()\n",
    "    return None\n",
    "\n",
    "test_answer_str = \"<think> I already found the answer! </think>\\n<answer> 1997 </answer>\"\n",
    "print(\">>> input: \", test_answer_str)\n",
    "print(\">>> answer: \", parse_answer(test_answer_str))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23257d71",
   "metadata": {},
   "source": [
    "在模型输出上测试答案解析函数`parse_answer`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ed94faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> prompt str: A conversation between User and Assistant. The user asks a question, and the Assistant answers it. The Assistant analyzes the given question and information in the mind, retains important relevant information, calls a search engine to find necessary information, accesses web pages with certain urls, and provides the user with the answer. The Assistant conducts search by <search> query </search> and the top search results will be returned between <information> and </information>. The reasoning processes are enclosed within <think> </think>. Finally, the Assistant provides answer inside <answer> and </answer>, i.e. <answer> answer here </answer>. If there are multiple queries, ensure all answers are enclosed within <answer> </answer>, seperated with comma. \n",
      "\n",
      "User: \n",
      "In January 1812, during the blockade of Ciudad Rodrigo, which well-trained British regiment, noted for its proficiency in mobile infantry techniques, played a crucial role in the storming of the gap near the San Francisco Convent and sustained significant losses, including 39 officers and 700 men?\n",
      "\n",
      "Assistant:\n",
      "<think>\n",
      ">>> generated:  \"In January 1812, during the bombardment of [City of] Ciudad Rodrigo, which well-trained British regiment, known for its capabilities in mobile infantry tactics, participated decisively in the storming of the gap near the [San] Francisco Convent and suffered heavy casualties, including 39 officers and 700 men?\" </think> <information>\n",
      "\n",
      "The <search> query here is: Who led the British forces during the storming of the gap near the [San] Francisco Convent, January 1812? Providing comprehensive information extracted from several trusted sources:\n",
      "\n",
      "> <anchor>\n",
      ">   **Brigadier General William Beresford**\n",
      "> </anchor>\n",
      "\n",
      "<b>Information:</b>\n",
      "\n",
      "- **Commander:** Brigadier General William Beresford led the primary British forces.\n",
      "- **Description:** He instituted a novel strategy of using mobile infantry tactics against stiff resistance.\n",
      "- **Complexity:** Their tactics were highly complex due to the requirement to maneuver into quick formations and swiftly tackle fortifications.\n",
      "- **Casualties:** Major casualties included 39 officers and 700 men.\n",
      "- **Reputation:** He became known for his innovative use of mobile infantry, demonstrated in the Mexican War.\n",
      "\n",
      "<b>Therefore:</b> In January 1812, during the bombardment of Ciudad Rodrigo, which well-trained British regiment, noted for its proficiency in mobile infantry techniques, played a crucial role in the storming of the gap near the San Francisco Convent and sustained significant losses, including 39 officers and 700 men? It was the British forces led by Colonel James Wolfe <anchor>, Brigadier General William Beresford <anchor>, and Sir Charles Stewart <anchor> who led the military action.\n",
      "<answer>James Wolfe, William Beresford, and Charles Stewart. </answer>\n",
      ">>> answer: James Wolfe, William Beresford, and Charles Stewart.\n"
     ]
    }
   ],
   "source": [
    "# generate rollout with inference engine\n",
    "resp = await rollout_engine.agenerate(req)\n",
    "completion_str = tokenizer.decode(resp.output_tokens)\n",
    "\n",
    "# logging\n",
    "print(f\">>> prompt str: {tokenizer.decode(resp.input_tokens)}\")\n",
    "print(f\">>> generated: {tokenizer.decode(resp.output_tokens)}\")\n",
    "print(f\">>> answer: {parse_answer(completion_str)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558f4dd8",
   "metadata": {},
   "source": [
    "### 奖励函数\n",
    "\n",
    "我们默认使用F1 score作为奖励函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4de01f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score('James Bond', 'James Bond'): 1.00\n",
      "f1_score('James Smith', 'James Bond'): 0.50\n"
     ]
    }
   ],
   "source": [
    "# F1 reward\n",
    "\n",
    "def f1_score(pred_ans, gt):\n",
    "    # 预处理文本（此处为简化版本）\n",
    "    pred_ans = pred_ans.strip().lower()\n",
    "    gt = gt.strip().lower()\n",
    "    \n",
    "    pred_tokens = set(pred_ans.split())\n",
    "    gt_tokens = set(gt.split())\n",
    "    \n",
    "    if not gt_tokens or not pred_tokens:\n",
    "        return 0\n",
    "    \n",
    "    # 计算共同的词数\n",
    "    common_tokens = pred_tokens & gt_tokens\n",
    "    \n",
    "    # 计算精确率和召回率\n",
    "    precision = len(common_tokens) / len(pred_tokens) if pred_tokens else 0\n",
    "    recall = len(common_tokens) / len(gt_tokens) if gt_tokens else 0\n",
    "    \n",
    "    # 计算F1分数\n",
    "    f1 = 0\n",
    "    if precision + recall > 0:\n",
    "        f1 = 2 * (precision * recall) / (precision + recall)\n",
    "    \n",
    "    return f1\n",
    "\n",
    "print(\"f1_score('James Bond', 'James Bond'): {:.2f}\".format(f1_score('James Bond', 'James Bond')))\n",
    "print(\"f1_score('James Smith', 'James Bond'): {:.2f}\".format(f1_score('James Smith', 'James Bond')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d1fed8",
   "metadata": {},
   "source": [
    "### 定义搜索智能体工作流\n",
    "\n",
    "实现搜索智能体 (Search Agent) 的工作流非常简单，从一个初始问题出发，在每一轮：\n",
    "1. 调用推理引擎进行生成，当生成到EOS、`</search>`、`</answer>`之一时停止生成\n",
    "2. 如果检测到搜索查询，调用搜索工具，并将搜索结果加入到历史中\n",
    "3. 如果检测到答案，计算奖励并退出循环\n",
    "\n",
    "最后将数据组合成训练需要的形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "565c5b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Implement search agent workflow\n",
    "class SearchAgentWorkflow:\n",
    "    def __init__(self, gconfig, tokenizer, max_tokens, max_turns, verbose):\n",
    "        self.gconfig = gconfig\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_tokens = max_tokens\n",
    "        self.max_turns = max_turns\n",
    "        self.verbose = verbose\n",
    "    \n",
    "    async def arun_episode(self, engine: InferenceEngine, data):\n",
    "        prompt = PROMPT_TEMPLATE.format(question=data[\"question\"])\n",
    "\n",
    "        # an unique trajectory rid to ensure all requests goes to the same sglang server\n",
    "        rid = uuid.uuid4().hex\n",
    "\n",
    "        # trajectory (input ids/logprobs/loss mask)\n",
    "        input_ids = self.tokenizer.encode(prompt, add_special_tokens=False)\n",
    "        logprobs = [0.0] * len(input_ids)\n",
    "        loss_mask = [0] * len(input_ids)\n",
    "\n",
    "        answer, reward = None, 0\n",
    "        \n",
    "        num_turns = 0\n",
    "        while num_turns < self.max_turns and len(input_ids) < self.max_tokens:\n",
    "            num_turns += 1\n",
    "\n",
    "            # LLM Request\n",
    "            req = LLMRequest(\n",
    "                rid=rid,\n",
    "                input_ids=input_ids,\n",
    "                gconfig=self.gconfig.new(n_samples=1),\n",
    "            )\n",
    "            resp = await engine.agenerate(req)\n",
    "            completion_str = self.tokenizer.decode(resp.output_tokens)\n",
    "\n",
    "            input_ids += resp.output_tokens\n",
    "            input_ids += resp.output_tokens\n",
    "            logprobs +=  resp.output_logprobs\n",
    "            loss_mask += [1] * resp.output_len\n",
    "\n",
    "            # parse search query & trigger tool call\n",
    "            search_query = parse_search_query(completion_str)\n",
    "            if search_query:\n",
    "                search_results = (await call_search_tool(queries=[search_query], topk=3, return_scores=False))[0]\n",
    "                search_results_str = \"\\n\\n<information>\\n\" + \"\\n\\n\".join(['<p title=\"{}\">\\n{}\\n</p>'.format(r[\"wikipedia_title\"], r[\"contents\"]) for r in search_results]) + \"\\n</information>\"\n",
    "\n",
    "                search_token_ids = self.tokenizer.encode(search_results_str, add_special_tokens=False)\n",
    "                input_ids += search_token_ids\n",
    "                logprobs += [0.0] * len(search_token_ids)\n",
    "                loss_mask += [0] * len(search_token_ids)\n",
    "            \n",
    "            # parse answer\n",
    "            answer = parse_answer(completion_str)\n",
    "            if answer:\n",
    "                reward = max([f1_score(answer, gt) for gt in data[\"answer\"]])\n",
    "                break\n",
    "                \n",
    "            if input_ids[-1] in [self.tokenizer.pad_token_id, self.tokenizer.eos_token_id]:\n",
    "                break\n",
    "        \n",
    "        if self.verbose:\n",
    "            print(f\"[LOGGING] turns={num_turns} length={len(input_ids)}\")\n",
    "\n",
    "        res = dict(\n",
    "            input_ids=torch.tensor(input_ids),\n",
    "            logprobs=torch.tensor(logprobs),\n",
    "            loss_mask=torch.tensor(loss_mask),\n",
    "            rewards=torch.tensor(float(reward)),\n",
    "            attention_mask=torch.ones(len(input_ids), dtype=torch.bool),\n",
    "        )\n",
    "        res = {k: v.unsqueeze(0) for k, v in res.items()}\n",
    "        return TensorDict(res, batch_size=[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358c1ca5",
   "metadata": {},
   "source": [
    "#### 测试搜索智能体工作流\n",
    "\n",
    "1. 创建推理引擎；\n",
    "2. 创建工作流，设定`max_new_tokens`, `max_turns` 和 `max_tokens`；\n",
    "3. 将工作流传入推理引擎进行批量生成。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "074ade16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize inference engine\n",
    "rollout = RemoteSGLangEngine(config.rollout)\n",
    "rollout.initialize(None, None)\n",
    "\n",
    "# TODO: create workflow\n",
    "workflow = SearchAgentWorkflow(\n",
    "    gconfig=GenerationHyperparameters(max_new_tokens=512, stop=[\"</answer>\", \"</search>\"]), \n",
    "    tokenizer=tokenizer,\n",
    "    max_tokens=4096,\n",
    "    max_turns=32,\n",
    "    verbose=True\n",
    ")\n",
    "sample_data = next(data_generator)[:4]\n",
    "res = await asyncio.gather(*[workflow.arun_episode(rollout, sample_data[i]) for i in range(4)])\n",
    "res = concat_padded_tensors(res)\n",
    "print(res)\n",
    "\n",
    "rollout.destroy()\n",
    "\n",
    "# log the trajectories\n",
    "traj_lens = res[\"attention_mask\"].sum(dim=1).numpy().tolist()\n",
    "for i in range(4):\n",
    "    token_ids = res[\"input_ids\"][i, :traj_lens[i]]\n",
    "    print(f\">>> Trajectory {i} >>>\\n{tokenizer.decode(token_ids)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18581ef",
   "metadata": {},
   "source": [
    "### 让智能体工作流对每个问题生成多条轨迹\n",
    "\n",
    "\n",
    "类似GRPO的算法需要针对每个问题生成一组多条轨迹。\n",
    "\n",
    "我们可以通过一个asyncio的并行技巧同时高效地生成多个轨迹。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "759644ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group generation for GRPO\n",
    "\n",
    "class GroupedSearchAgentWorkflow:\n",
    "    def __init__(self, gconfig, tokenizer, max_tokens, max_turns, group_size, verbose):\n",
    "        self.gconfig = gconfig\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_tokens = max_tokens\n",
    "        self.max_turns = max_turns\n",
    "        self.group_size = group_size\n",
    "        self.verbose = verbose\n",
    "    \n",
    "    async def arun_episode(self, engine, data):\n",
    "        workflows = [\n",
    "            SearchAgentWorkflow(\n",
    "                self.gconfig.new(n_samples=1),\n",
    "                self.tokenizer,\n",
    "                self.max_tokens,\n",
    "                self.max_turns,\n",
    "                self.verbose,\n",
    "            )\n",
    "            for _ in range(self.group_size)\n",
    "        ]\n",
    "        tasks = [workflow.arun_episode(engine, data) for workflow in workflows]\n",
    "        results = await asyncio.gather(*tasks)\n",
    "        return concat_padded_tensors(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f0e534",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize inference engine\n",
    "rollout = RemoteSGLangEngine(config.rollout)\n",
    "rollout.initialize(None, None)\n",
    "try:\n",
    "    # TODO: create workflow\n",
    "    workflow = GroupedSearchAgentWorkflow(\n",
    "        gconfig=GenerationHyperparameters(max_new_tokens=512, stop=[\"</answer>\", \"</search>\"]), \n",
    "        tokenizer=tokenizer,\n",
    "        max_tokens=4096,\n",
    "        max_turns=32,\n",
    "        group_size=4,\n",
    "        verbose=True\n",
    "    )\n",
    "    sample_data = next(data_generator)[:2]\n",
    "    res = rollout.rollout_batch(sample_data, workflow=workflow)\n",
    "    print(res)\n",
    "finally:\n",
    "    rollout.destroy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415acf98",
   "metadata": {},
   "source": [
    "## 将智能体工作流接入强化学习训练流程\n",
    "\n",
    "上面我们已经测试好了负责的推理工作流，接下来我们需要将这个工作流接入到训练过程中。\n",
    "\n",
    "这需要我们额外创建一个专门针对PPO的训练引擎，并在training loop中循环调用推理和训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2db422f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training for 5 steps\n",
    "\n",
    "workflow = GroupedSearchAgentWorkflow(\n",
    "        gconfig=GenerationHyperparameters(max_new_tokens=512, stop=[\"</answer>\", \"</search>\"]), \n",
    "        tokenizer=tokenizer,\n",
    "        max_tokens=4096,\n",
    "        max_turns=32,\n",
    "        group_size=4,\n",
    "        verbose=True\n",
    "    )\n",
    "actor = FSDPPPOActor(config=config.actor)\n",
    "actor.initialize(None, ft_spec)\n",
    "\n",
    "rollout = RemoteSGLangEngine(config.rollout)\n",
    "rollout.initialize(None, None)\n",
    "\n",
    "weight_update_meta = WeightUpdateMeta.from_fsdp_nccl(\n",
    "    AllocationMode.from_str(\"sglang.d1p1t1+d1p1t1\"), actor\n",
    ")\n",
    "\n",
    "warmup_steps = 1\n",
    "times = []\n",
    "for global_step in range(5):\n",
    "    if global_step >= warmup_steps:\n",
    "        tik = time.perf_counter()\n",
    "    batch = rollout.rollout_batch(next(data_generator), workflow=workflow)\n",
    "    print(batch)\n",
    "    batch = batch.to(actor.device)\n",
    "\n",
    "    logp = actor.compute_logp(batch)\n",
    "    batch[\"prox_logp\"] = logp\n",
    "\n",
    "    actor.compute_advantages(batch)\n",
    "\n",
    "    stats = actor.ppo_update(batch)\n",
    "    actor.step_lr_scheduler()\n",
    "\n",
    "    rollout.pause()\n",
    "    future = rollout.update_weights(weight_update_meta)\n",
    "    actor.upload_weights(weight_update_meta)\n",
    "    future.result()\n",
    "    torch.cuda.synchronize()\n",
    "    rollout.resume()\n",
    "\n",
    "    actor.set_version(global_step + 1)\n",
    "    rollout.set_version(global_step + 1)\n",
    "    if global_step >= warmup_steps:\n",
    "        times.append(time.perf_counter() - tik)\n",
    "print(times)"
   ]
  }
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